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A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data

dc.contributor.authorGayo Avello, Daniel 
dc.date.accessioned2014-04-04T08:14:25Z
dc.date.available2014-04-04T08:14:25Z
dc.date.issued2013
dc.identifier.citationSocial Science Computer Review, 31(6), p. 649-679 (2013); doi:10.1177/0894439313493979
dc.identifier.issn0894-4393
dc.identifier.issn1552-8286
dc.identifier.urihttp://hdl.handle.net/10651/25244
dc.description.abstractElectoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this article aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) propose some considerations to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been somewhat exaggerated: Social media may provide a glimpse on electoral outcomes but, up to now, research has not provided strong evidence to support it can currently replace traditional polls. Nevertheless, there are some reasons for optimism and, hence, further work on this topic is required, along with tighter integration with traditional electoral forecasting research
dc.description.sponsorshipThe author(s) received no financial support for the research, authorship, and/or publication of this article.
dc.format.extentp. 649-679
dc.language.isoeng
dc.publisherSage
dc.relation.ispartofSocial Science Computer Review, 31(6)
dc.rights© D. Gayo Avello
dc.rights© Sage
dc.subjectTwitter
dc.subjectSocial Media
dc.titleA Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Dataeng
dc.typeinfo:eu-repo/semantics/article
dc.identifier.local20141105
dc.identifier.doi10.1177/0894439313493979
dc.type.dcmitext
dc.relation.publisherversionhttp://dx.doi.org/10.1177/0894439313493979


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